Question

我正在尝试绘制Tukey测试的结果，但我正在努力将数据放入基于P值的组中。我试图复制的This is the equivalent in R。我一直在使用SciPy单向ANOVA测试和Tukey测试statsmodel，但不能以相同的方式完成这些组。

非常感谢任何帮助

我刚刚发现这个another example in R of what I want to do in python

Answer 1

我一直在努力做同样的事情。我找到了一篇论文，告诉您如何对字母进行编码。
Hans-Peter Piepho（2004）一种全字母比较的基于字母表示的算法，计算与图形统计学报，13：2，456-466，DOI：10.1198 / 1061860043515

进行编码有点棘手，因为您需要检查和复制列，然后合并列。我试图给感冒添加一些评论。我想出了一种方法，您可以在其中运行tukeyhsd，然后从结果中计算字母。应该可以将其转换为功能。或希望是tukeyhsd的一部分。我的数据没有发布，但它是一列数据，然后是描述这些组的列。对我来说，这些团体是纽约的五个区。您也可以只更改注释并在第一次使用随机数据。

# Read data.  Comment out the next ones to use random data.  
df=pd.read_excel('anova_test.xlsx')
#n=1000
#df = pd.DataFrame(columns=['Groups','Data'],index=np.arange(n))
#df['Groups']=np.random.randint(1, 4,size=n)
#df['Data']=df['Groups']*np.random.random_sample(size=n)


# define columns for data and then grouping
col_to_group='Groups'
col_for_data='Data'

#Now take teh data and regroup for anova
samples = [cols[1] for cols in df.groupby(col_to_group)[col_for_data]]    #I am not sure how this works but it makes an numpy array for each group 
f_val, p_val = stats.f_oneway(*samples)  # I am not sure what this star does but this passes all the numpy arrays correctly
#print('F value: {:.3f}, p value: {:.3f}\n'.format(f_val, p_val))

# this if statement can be uncommmented if you don't won't to go furhter with out p<0.05
#if p_val<0.05:    #If the p value is less than 0.05 it then does the tukey
mod = MultiComparison(df[col_for_data], df[col_to_group])
thsd=mod.tukeyhsd()
#print(mod.tukeyhsd())

#this is a function to do Piepho method.  AN Alogrithm for a letter based representation of al-pairwise comparisons.  
tot=len(thsd.groupsunique)
#make an empty dataframe that is a square matrix of size of the groups. #set first column to 1
df_ltr=pd.DataFrame(np.nan, index=np.arange(tot),columns=np.arange(tot))
df_ltr.iloc[:,0]=1
count=0
df_nms = pd.DataFrame('', index=np.arange(tot), columns=['names'])  # I make a dummy dataframe to put axis labels into.  sd stands for signifcant difference

for i in np.arange(tot):   #I loop through and make all pairwise comparisons. 
    for j in np.arange(i+1,tot):
        #print('i=',i,'j=',j,thsd.reject[count])
        if thsd.reject[count]==True:
            for cn in np.arange(tot):
                if df_ltr.iloc[i,cn]==1 and df_ltr.iloc[j,cn]==1: #If the column contains both i and j shift and duplicat
                    df_ltr=pd.concat([df_ltr.iloc[:,:cn+1],df_ltr.iloc[:,cn+1:].T.shift().T],axis=1)
                    df_ltr.iloc[:,cn+1]=df_ltr.iloc[:,cn]
                    df_ltr.iloc[i,cn]=0
                    df_ltr.iloc[j,cn+1]=0
                #Now we need to check all columns for abosortpion.
                for cleft in np.arange(len(df_ltr.columns)-1):
                    for cright in np.arange(cleft+1,len(df_ltr.columns)):
                        if (df_ltr.iloc[:,cleft].isna()).all()==False and (df_ltr.iloc[:,cright].isna()).all()==False: 
                            if (df_ltr.iloc[:,cleft]>=df_ltr.iloc[:,cright]).all()==True:  
                                df_ltr.iloc[:,cright]=0
                                df_ltr=pd.concat([df_ltr.iloc[:,:cright],df_ltr.iloc[:,cright:].T.shift(-1).T],axis=1)
                            if (df_ltr.iloc[:,cleft]<=df_ltr.iloc[:,cright]).all()==True:
                                df_ltr.iloc[:,cleft]=0
                                df_ltr=pd.concat([df_ltr.iloc[:,:cleft],df_ltr.iloc[:,cleft:].T.shift(-1).T],axis=1)

        count+=1

#I sort so that the first column becomes A        
df_ltr=df_ltr.sort_values(by=list(df_ltr.columns),axis=1,ascending=False)

# I assign letters to each column
for cn in np.arange(len(df_ltr.columns)):
    df_ltr.iloc[:,cn]=df_ltr.iloc[:,cn].replace(1,chr(97+cn)) 
    df_ltr.iloc[:,cn]=df_ltr.iloc[:,cn].replace(0,'')
    df_ltr.iloc[:,cn]=df_ltr.iloc[:,cn].replace(np.nan,'') 

#I put all the letters into one string
df_ltr=df_ltr.astype(str)
df_ltr.sum(axis=1)
#print(df_ltr)
#print('\n')
#print(df_ltr.sum(axis=1))

#Now to plot like R with a violing plot
fig,ax=plt.subplots()
df.boxplot(column=col_for_data, by=col_to_group,ax=ax,fontsize=16,showmeans=True
                    ,boxprops=dict(linewidth=2.0),whiskerprops=dict(linewidth=2.0))  #This makes the boxplot

ax.set_ylim([-10,20])

grps=pd.unique(df[col_to_group].values)   #Finds the group names
grps.sort() # This is critical!  Puts the groups in alphabeical order to make it match the plotting

props=dict(facecolor='white',alpha=1)
for i,grp in enumerate(grps):   #I loop through the groups to make the scatters and figure out the axis labels. 

    x = np.random.normal(i+1, 0.15, size=len(df[df[col_to_group]==grp][col_for_data]))
    ax.scatter(x,df[df[col_to_group]==grp][col_for_data],alpha=0.5,s=2)
    name="{}\navg={:0.2f}\n(n={})".format(grp
                            ,df[df[col_to_group]==grp][col_for_data].mean()
                            ,df[df[col_to_group]==grp][col_for_data].count())
    df_nms['names'][i]=name 
    ax.text(i+1,ax.get_ylim()[1]*1.1,df_ltr.sum(axis=1)[i],fontsize=10,verticalalignment='top',horizontalalignment='center',bbox=props)


ax.set_xticklabels(df_nms['names'],rotation=0,fontsize=10)
ax.set_title('')
fig.suptitle('')

fig.savefig('anovatest.jpg',dpi=600,bbox_inches='tight')

Results showing the letters above plots using the tukeyhsd

在SciPy中进行Tukey-Test分组和绘图

1 个答案: